##
# Replication files for Stewart and Zhukov 2009
##

# Use of force and civil�military relations in Russia:
#  an automated content analysis
# By: Brandon Stewart and Yuri Zhukov
http://scholar.harvard.edu/bstewart
http://scholar.harvard.edu/zhukov

NB:
I am assembling these replication archives considerably after the fact and thus can't be entirely sure that the packages on which these analyses were based still function in the same way.  Zelig for example has undergone considerable revisions and thus I would not expect the code to run clearly with the current version.
For those solely interested in the article for the methodological approach I've listed some citations below the file descriptions that may be useful.
For those solely interested in the article for the data- note that we have included all of the data collected including variables note appearing in the article.  However, we are unable to include the raw texts of the speeches due to intellectual property restrictions.  However the appendix has a complete listing such that the corpus could be effectively reconstructed.
- Brandon Stewart, 10/29/2013

##
# File Descriptions
##
1) Article.pdf  
print of the actual article

2) Appendix.pdf 
detailed codebook on data and methods

3) ModelsandFigures 
folder containing R code to reproduce the figures in the paper along with necessary data files.  090206_cm.csv is the major data file with the others providing information derived from machine learning on the texts

4) MachineLearning/Supervised
folder containing R code and feature matrices for the Activist/Conservative classification scheme.

5) MachineLearning/Unsupervised
folder containing R code and feature matrices for running Grimmer's Expressed Agenda Model on the speeches and subsequent classification into the Realpolitik/Interventionist/Other categores.

##
# Citations
##
Some thoughts several years after the fact - Brandon Stewart, 10/29/2013

Those interested in the content analysis approach may find my (later) review article helpful.

Grimmer J, Stewart BM. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis. 2013;21(3):267-297

Had it existed at the the time that we wrote the article we likely would have used the SuperLearner package in R to do the ensemble classifier rather than the custom setup we used here.

For those interested in the Expressed Agenda model and unsupervised methods you may find my lecture at the Tools for Text workshop and associated lab files to be helpful:
http://toolsfortext.wordpress.com/readings-and-software/
I also recommend that those looking to use the Expressed Agenda model use the later code available from Justin Grimmer's dataverse page rather than the replication code available here which was an earlier version of the model.


   